3Parallel magnetic resonance imaging with adaptive radius in <i>k</i>‐space (PARS): Constrained image reconstruction using <i>k</i>‐space locality in radiofrequency coil encoded data

Yeh, Ernest N.; McKenzie, Charles A.; Ohliger, Michael A.; Sodickson, Daniel K.

doi:10.1002/mrm.20490

Cited by 91 publications

(120 citation statements)

References 24 publications

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“…Parallel image reconstruction may still be performed, albeit with some increase in computational burden and reconstruction time, using the generalized reconstruction approaches used in this work or alternative algorithms reported elsewhere (36).…”

Section: Discussionmentioning

confidence: 99%

Toward single breath‐hold whole‐heart coverage coronary MRA using highly accelerated parallel imaging with a 32‐channel MR system

Niendorf

Hardy

Giaquinto

et al. 2006

Magnetic Resonance in Med

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117

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Coronary MR angiography (CMRA) is generally confined to the acquisition of multiple targeted slabs with coverage dictated by the competing constraints of signal-to-noise ratio (SNR), physiological motion, and scan time. This work addresses these obstacles by demonstrating the technical feasibility of using a 32-channel coil array and receiver system for highly accelerated volumetric breath-hold CMRA. The use of the 32-element array in unaccelerated CMRA studies provided a baseline SNR increase of as much as 40% over conventional cardiac-optimized phased array coils, which resulted in substantially enhanced image quality and improved delineation of the coronary arteries. Modest accelerations were used to reduce breath-hold durations for tailored coverage of the coronary arteries using targeted multi-oblique slabs to as little as 10 s. Finally, high net accelerations were combined with the SNR advantages of a 3D steady-state free precession (SSFP) technique to achieve previously unattainable comprehensive volumetric coverage of the coronary arteries in a single breath-hold. The merits and limitations of this simplified volumetric imaging approach are discussed and its implications for coronary MRA are considered. Magn Reson Med 56:167-176, 2006.

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Section: Discussionmentioning

confidence: 99%

Toward single breath‐hold whole‐heart coverage coronary MRA using highly accelerated parallel imaging with a 32‐channel MR system

Niendorf

Hardy

Giaquinto

et al. 2006

Magnetic Resonance in Med

Self Cite

117

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“…After all rows of M ؉ are computed, G ؉ is calculated following Eq. [12]. Specifically, each element of G ؉ is computed as,…”

Section: The Kspa Algorithmmentioning

confidence: 99%

“…Another image-based algorithm named "sensitivity profiles from an array of coils for encoding and reconstruction in parallel" (SPACE RIP) has also been introduced by Kyriakos et al (7) to reconstruct an image column-by-column along the phase encoding direction. k-Space algorithms include, for example, simultaneous acquisition of spatial harmonics (SMASH) by Sodickson and Manning (8) and generalized GRAPPA by Bydder et al (9), generalized autocalibrating partially parallel acquisitions (GRAPPA) by Griswold et al (10,11), and parallel imaging with adaptive radius in kspace (PARS) by Yeh et al (12), among other methods (13,14). The iterative SENSE algorithm expressed the kspace data as a linear combination of the spatially-encoded magnetization in which the multiplicative spatial encoding function is the product of coil sensitivity and the Fourier encoding function.…”

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confidence: 99%

Parallel imaging reconstruction for arbitrary trajectories using k‐space sparse matrices (kSPA)

Liu

Bammer

Moseley

2007

Magnetic Resonance in Med

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Although the concept of receiving MR signal using multiple coils simultaneously has been known for over two decades, the technique has only recently become clinically available as a result of the development of several effective parallel imaging reconstruction algorithms. Despite the success of these algorithms, it remains a challenge in many applications to rapidly and reliably reconstruct an image from partially-acquired general non-Cartesian k-space data. Such applications include, for example, three-dimensional (3D) imaging, functional MRI (fMRI), perfusion-weighted imaging, and diffusion tensor imaging (DTI), in which a large number of images have to be reconstructed. In this work, a systematic k-space-based reconstruction algorithm based on k-space sparse matrices (kSPA) is introduced. This algorithm formulates the image reconstruction problem as a system of sparse linear equations in k-space. The inversion of this system of equations is achieved by computing a sparse approximate inverse matrix. The algorithm is demonstrated using both simulated and in vivo data, and the resulting image quality is comparable to that of the iterative sensitivity encoding (SENSE) algorithm. The kSPA algorithm is noniterative and the computed sparse approximate inverse can be applied repetitively to reconstruct all subsequent images. This algorithm, therefore, is particularly suitable for the aforementioned applications.

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“…To reduce scan duration, two different types of techniques are often used. The first one is the multiple-coil based parallel MR imaging (pMRI) schemes, which have been actively investigated over the recent years using a variety of methods [1][2][3][4][5][6][7][8][9][10][11][12]. Some of these pMRI methods require the coil sensitivities to be known explicitly, while others reconstruct images with an implicit usage of the coil sensitivity information, through an auto-calibration process.…”

Section: Introductionmentioning

confidence: 99%

Improved l1-SPIRiT using 3D walsh transform-based sparsity basis

Zhao¹,

F²,

Jiang³

et al. 2014

Magnetic Resonance Imaging

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l1-SPIRiT is a fast magnetic resonance imaging (MRI) method which combines parallel imaging (PI)with compressed sensing (CS) by performing a joint l1-norm and l2-norm optimization procedure.The original l1-SPIRiT method uses two-dimensional (2D) Wavelet transform to exploit the intra-coil data redundancies and a joint sparsity model to exploit the inter-coil data redundancies. In this work, we propose to stack all the coil images into a three-dimensional (3D) matrix, and then a novel 3D Walsh transform-based sparsity basis is applied to simultaneously reduce the intra-coil and inter-coil data redundancies. Both the 2D Wavelet transform-based and the proposed 3D Walsh transform-based sparsity bases were investigated in the l1-SPIRiT method. The experimental results show that the proposed 3D Walsh transform-based l1-SPIRiT method outperformed the original l1-SPIRiT in terms of image quality and computational efficiency.

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3Parallel magnetic resonance imaging with adaptive radius in k‐space (PARS): Constrained image reconstruction using k‐space locality in radiofrequency coil encoded data

Cited by 91 publications

References 24 publications

Toward single breath‐hold whole‐heart coverage coronary MRA using highly accelerated parallel imaging with a 32‐channel MR system

Toward single breath‐hold whole‐heart coverage coronary MRA using highly accelerated parallel imaging with a 32‐channel MR system

Parallel imaging reconstruction for arbitrary trajectories using k‐space sparse matrices (kSPA)

Improved l1-SPIRiT using 3D walsh transform-based sparsity basis

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